BioNER predates CS NER. It was one of the earliest domains taken up for text mining of fine-grained entities from scholarly publications to enhance search engine performance in health applications. It aims to recognize concepts in bioscience and medicine. E.g., protein, gene, disease, drug, tissue, body part and location of activity such as cell or organism. The most frequently used corpora are GENETAG (full-text articles annotated with protein/gene entities) [1]}, JNLPBA (2400 abstracts annotated with DNA, RNA, protein, cell type and cell line concepts) [2]}, GENIA (200 Medline abstracts annotated with 36 different concepts from the Genia ontology and several levels of linguistic/semantic features) [3]}, NCBI disease corpus (793 abstracts annotated with diseases in the MeSH taxonomy) [4]}, CRAFT (the second largest corpus with 97 full text papers annotated with over 4000 corpus) [5]} linking to the NCBI Taxonomy, the Protein, Gene, Cell, Sequence ontologies etc. Finally, the MedMentions corpus [6]} as the largest dataset with 4000 abstracts with 34,724 concepts from the UMLS ontology. By leveraging ontologies such as the Gene Ontology [7]}, UMLS [8]}, MESH, or the NCBI Taxonomy [9]}, for the semantic concepts, these corpora build on years of careful knowledge representation work and are semantically consistent with a wide variety of other efforts that exploit these community resources. This differs from CS NER which is evolving toward standardized concepts.
